Changes in brain functional asymmetry are important physiological characteristics for evaluating neurorehabilitation. The characteristics of brain networks can be used to assess the brain functional asymmetry. The multiplex networks is defined as a multilayer networks that the interlayer connections are not present, apart from those between replica nodes. How to integrate different single layer networks information to assess the asymmetry for enhancing the assessment accuracy of the neurorehabilitation is a problem that attracts our attention. BAMN, a brain asymmetry analysis method based on multiplex network is presented in this paper. The proposed method extends the attributes of graph theory of single layer to multilayer, calculates their differences between the left and right hemisphere of the brain. It has been validated by using clinical EEG data of after anterior cruciate ligament reconstruction (ACLR) patients and healthy controls, discovering the distinct asymmetry features between these two groups. Some of the signifcant difference features are significantly correlated with the clinical scores and they may be used in the future assessment of the neurorehabilitation.

With the diminishing return from Moore’s Law, system-technology co-optimization (STCO) has emerged as a promising approach to sustain the scaling trends in the VLSI industry. By bridging the gap between system requirements and technology innovations, STCO enables customized optimizations for application-driven system architectures. However, existing research lacks sufficient discussion on efficient STCO methodologies, particularly in addressing the information gap across design hierarchies and navigating the expansive cross-layer design space. To address these challenges, this paper presents Orthrus, a dual-loop automated framework that synergizes system-level and technology-level optimizations. At the system level, Orthrus employs a novel mechanism to prioritize the optimization of critical standard cells using system-level statistics. It also guides technology-level optimization via the normal directions of the Pareto frontier efficiently explored by Bayesian optimization. At the technology level, Orthrus leverages system-aware insights to optimize standard cell libraries. It employs a neural network-assisted enhanced differential evolution algorithm to efficiently optimize technology parameters. Experimental results on 7nm technology demonstrate that Orthrus achieves 12.5% delay reduction at iso-power and 61.4% power savings at iso-delay over the baseline approaches, establishing new Pareto frontiers in STCO.

MOTIVATION: Spatial clustering is essential and challenging for spatial transcriptomics' data analysis to unravel tissue microenvironment and biological function. Graph neural networks are promising to address gene expression profiles and spatial location information in spatial transcriptomics to generate latent representations. However, choosing an appropriate graph deep learning module and graph neural network necessitates further exploration and investigation. RESULTS: In this article, we present GRAPHDeep to assemble a spatial clustering framework for heterogeneous spatial transcriptomics data. Through integrating 2 graph deep learning modules and 20 graph neural networks, the most appropriate combination is decided for each dataset. The constructed spatial clustering method is compared with state-of-the-art algorithms to demonstrate its effectiveness and superiority. The significant new findings include: (i) the number of genes or proteins of spatial omics data is quite crucial in spatial clustering algorithms; (ii) the variational graph autoencoder is more suitable for spatial clustering tasks than deep graph infomax module; (iii) UniMP, SAGE, SuperGAT, GATv2, GCN, and TAG are the recommended graph neural networks for spatial clustering tasks; and (iv) the used graph neural network in the existent spatial clustering frameworks is not the best candidate. This study could be regarded as desirable guidance for choosing an appropriate graph neural network for spatial clustering. AVAILABILITY AND IMPLEMENTATION: The source code of GRAPHDeep is available at https://github.com/narutoten520/GRAPHDeep. The studied spatial omics data are available at https://zenodo.org/record/8141084.

Lining Zhang, Simon Mille, Yufang Hou, Daniel Deutsch, Elizabeth Clark, Yixin Liu, Saad Mahamood, Sebastian Gehrmann, Miruna Clinciu, Khyathi Raghavi Chandu, João Sedoc. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

This work proposes a statistical modeling approach for the artificial neural network (ANN)-based compact model (CM). The method of retaining part of the network features of the nominal device and further finetuning the network parameters (variational neurons) is found to accurately reproduce the static variation. A mapping from process variation to network parameters is derived by combining the proposed variational neuron selection algorithm and the backward propagation of variance (BPV) method. In addition, a secondary classification of the selected variational neurons is applied to model the fabrication-induced correlation between n- and p-type devices. The neural network-based statistical modeling approach has been well implemented and verified on the GAA simulation data and the 16nm node foundry FinFET, which indicates its great potential in modeling emerging and advanced device technology.

The development of X-Ray microscopy (XRM) technology has enabled non-destructive inspection of semiconductor structures for defect identification. Deep learning is widely used as the state-of-the-art approach to perform visual analysis tasks. However, deep learning based models require large amount of annotated data to train. This can be time-consuming and expensive to obtain especially for dense prediction tasks like semantic segmentation. In this work, we explore active learning (AL) as a potential solution to alleviate the annotation burden. We identify two unique challenges when applying AL on semiconductor XRM scans: large domain shift and severe class-imbalance. To address these challenges, we propose to perform contrastive pretraining on the unlabelled data to obtain the initialization weights for each AL cycle, and a rareness-aware acquisition function that favors the selection of samples containing rare classes. We evaluate our method on a semiconductor dataset that is compiled from XRM scans of high bandwidth memory structures composed of logic and memory dies, and demonstrate that our method achieves state-of-the-art performance.

Computationally intensive simulations have made analog circuit sizing challenging for complicated analog circuit performance characterization. Accurate yet computationally efficient data-driven models of circuit performance can potentially accelerate the design and verification process. However, as analog circuits are designed under strict functional and technology constraints, there is a scarcity of data for analog circuit performance classification, posing challenges to data-driven approaches; acquiring more data typically involves running expensive and time consuming simulations. We propose Bayesian Deep Active Learning (BDAL) to learn models using fewer simulations, by iteratively selecting a small number of informative samples to label based on the model uncertainty. Bayesian neural networks used in the BDAL framework are better able to model weight uncertainty while being sufficiently expressive to model complex circuits. Compared with the state-of-the-art approaches, the proposed BDAL method can obtain better classification performance with much fewer number of simulations. Experiments on four diverse analog circuits demonstrate BDAL can achieve significant reduction in data requirement and obtain similar performance with much less labeled data for analog circuit performance classification.

State-of-the-art methods for semantic segmentation are based on deep neural networks trained on large-scale labeled datasets. Acquiring such datasets would incur large annotation costs, especially for dense pixel-level prediction tasks like semantic segmentation. We consider region-based active learning as a strategy to reduce annotation costs while maintaining high performance. In this setting, batches of informative image regions instead of entire images are selected for labeling. Importantly, we propose that enforcing local spatial diversity is beneficial for active learning in this case, and to incorporate spatial diversity along with the traditional active selection criterion, e.g., data sample uncertainty, in a unified optimization framework for region-based active learning. We apply this framework to the Cityscapes and PASCAL VOC datasets and demonstrate that the inclusion of spatial diversity effectively improves the performance of uncertainty-based and feature diversity-based active learning methods. Our framework achieves 95% performance of fully supervised methods with only 5 - 9% of the labeled pixels, outperforming all state-of-the-art region-based active learning methods for semantic segmentation.

Makeup face verification in the wild is an important research problem for its popularization in real-world. However, little effort has been made to tackle it in computer vision. In this research, we first build a new database, i.e., Facial Beauty Database (FBD), which contains paired facial images of 8933 subjects without and with makeup in different real-world scenarios. To the best of our knowledge, FBD is the largest makeup face database to date compared with existing databases for facial makeup research. Moreover, we propose a new discriminative marginal metric learning (DMML) algorithm to deal with this problem in the wild. Inspired by the fact that interclass marginal faces are usually more discriminative than interclass nonmarginal faces in learning the discriminative metric space, we use the interclass marginal faces to depict the discriminative information. Simultaneously, we wish that those interclass marginal faces without makeup relations are separated from each other as far as possible, so that more discriminative information between facial images without and with makeup can be exploited for verification. Furthermore, since multiple features could provide comprehensive information in describing the facial representations from diverse points of view and extract more informative cues from facial images, we also introduce a multiview discriminative marginal metric learning (MDMML) algorithm by effectively learning a robust metric space such that multiple features from different points of view can be integrated to effectively enhance the performance of makeup face verification. Experimental results on two real-world makeup face databases are utilized to show the effectiveness of our method and the possibility of verifying the makeup relations from facial images in real-world.

Various machine learning and data mining tasks in classification require abundant data samples to be labeled for training. Conventional active learning methods aim at labeling the most informative samples for alleviating the labor of the user. Many previous studies in active learning select one sample after another in a greedy manner. However, this is not very effective because the classification models has to be retrained for each newly labeled sample. Moreover, many popular active learning approaches utilize the most uncertain samples by leveraging the classification hyperplane of the classifier, which is not appropriate since the classification hyperplane is inaccurate when the training data are small-sized. The problem of insufficient training data in real-world systems limits the potential applications of these approaches. This paper presents a novel method of active learning called manifold regularized experimental design (MRED), which can label multiple informative samples at one time for training. In addition, MRED gives an explicit geometric explanation for the selected samples to be labeled by the user. Different from existing active learning methods, our method avoids the intrinsic problems caused by insufficiently labeled samples in real-world applications. Various experiments on synthetic datasets, the Yale face database and the Corel image database have been carried out to show how MRED outperforms existing methods.

Human action recognition is an important problem in robotic vision. Traditional recognition algorithms usually require the knowledge of view angle, which is not always available in robotic applications such as active vision. In this paper, we propose a new framework to recognize actions with arbitrary views. A main feature of our algorithm is that view-invariance is learned from synthetic 2D and 3D training data using transfer dictionary learning. This guarantees the availability of training data, and removes the hassle of obtaining real world video in specific viewing angles. The result of the process is a dictionary that can project real world 2D video into a view-invariant sparse representation. This facilitates the training of a view-invariant classifier. Experimental results on the IXMAS and N-UCLA datasets show significant improvements over existing algorithms.

Content-based image retrieval (CBIR) has attracted much attention during the past decades for its potential practical applications to image database management. A variety of relevance feedback (RF) schemes have been designed to bridge the gap between low-level visual features and high-level semantic concepts for an image retrieval task. In the process of RF, it would be impractical or too expensive to provide explicit class label information for each image. Instead, similar or dissimilar pairwise constraints between two images can be acquired more easily. However, most of the conventional RF approaches can only deal with training images with explicit class label information. In this paper, we propose a novel discriminative semantic subspace analysis (DSSA) method, which can directly learn a semantic subspace from similar and dissimilar pairwise constraints without using any explicit class label information. In particular, DSSA can effectively integrate the local geometry of labeled similar images, the discriminative information between labeled similar and dissimilar images, and the local geometry of labeled and unlabeled images together to learn a reliable subspace. Compared with the popular distance metric analysis approaches, our method can also learn a distance metric but perform more effectively when dealing with high-dimensional images. Extensive experiments on both the synthetic data sets and a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of the CBIR.

Relevance feedback (RF) schemes have been widely designed to improve the performance of content-based image retrieval. Despite the success, it is not appropriate to require the user to label a large number of samples in RF. Collaborative image retrieval (CIR) aims to reduce the labeling efforts of the user by resorting to the auxiliary information. Support vector machine (SVM) active learning can select ambiguous samples as the most informative ones for the user to label with the help of the optimal hyperplane of SVM, and thus alleviate the labeling efforts of conventional RF. However, the optimal hyperplane of SVM is usually unstable and inaccurate with small-sized training data, and this is always the case in image retrieval since the user would not like to label a large number of feedback samples and cannot label each sample accurately all the time. In this paper, we propose a novel active learning method, i.e., geometric optimum experimental design (GOED), to select multiple representative samples in the database as the most informative ones for the user to label. Especially, GOED can alleviate the small-sized training data problem by leveraging the geometric structure of unlabeled samples in the reproducing kernel Hilbert space and thus further enhance the performance of image retrieval. Different from the conventional manifold regularization framework, the new method can effectively select the most informative samples for the user to label in image retrieval. By minimizing the expected average prediction variance on the test data, GOED has a clear geometric interpretation to select a set of the most representative samples in the database iteratively with the global optimum. Compared with the popular SVM active learning, our method is label-independent and can effectively avoid various potential problems caused by insufficient and inexactly labeled samples in RF, and is more appropriate and useful for image retrieval. Extensive experiments on both synthetic datasets and a real-world image database have been conducted to show the advantages of the proposed GOED for CIR.

Conventional content-based image retrieval (CBIR) systems with the Euclidean distance metric in a high-dimensional visual feature space usually cannot achieve satisfactory performance due to the semantic gap. Relevance feedback (RF) has been introduced as a powerful tool to involve the user in the system to improve the performance of CBIR. Despite the success, an on-line learning task can be tedious and boring for the user. Various schemes have been proposed to exploit the RF log data to further enhance the performance of CBIR. In this paper, we propose a semantic subspace learning (SSL) method to exploit the RF log data with contextual information for an image retrieval task. Different from conventional subspace learning approaches, our method can directly learn a semantic concept subspace from the RF log data with contextual information without using any class label information. We show that the performance of the image retrieval task can be significantly improved in the low-dimensional semantic concept subspace. Extensive experiments on a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of CBIR by exploiting the RF log data.

Content-based image retrieval (CBIR) has attracted substantial attention during the past few years for its potential applications. To bridge the gap between low level visual features and high level semantic concepts, various relevance feedback (RF) or interactive re-ranking (IR) schemes have been designed to improve the performance of a CBIR system. In this paper, we propose a novel subspace learning based IR scheme by using a graph embedding framework, termed Laplacian Regularized Subspace Learning (LRSL). The LRSL method can model both within-class compactness and between-class separation by specially designing an intrinsic graph and a penalty graph in the graph embedding framework, respectively. In addition, LRSL can share the popular assumption of the biased discriminant analysis (BDA) for IR but avoid the singular problem in BDA. Extensive experimental results have shown that the proposed LRSL method is effective for reducing the semantic gap and targeting the intentions of users for an image retrieval task.

With many potential practical applications, content-based image retrieval (CBIR) has attracted substantial attention during the past few years. A variety of relevance feedback (RF) schemes have been developed as a powerful tool to bridge the semantic gap between low-level visual features and high-level semantic concepts, and thus to improve the performance of CBIR systems. Among various RF approaches, support-vector-machine (SVM)-based RF is one of the most popular techniques in CBIR. Despite the success, directly using SVM as an RF scheme has two main drawbacks. First, it treats the positive and negative feedbacks equally, which is not appropriate since the two groups of training feedbacks have distinct properties. Second, most of the SVM-based RF techniques do not take into account the unlabeled samples, although they are very helpful in constructing a good classifier. To explore solutions to overcome these two drawbacks, in this paper, we propose a biased maximum margin analysis (BMMA) and a semisupervised BMMA (SemiBMMA) for integrating the distinct properties of feedbacks and utilizing the information of unlabeled samples for SVM-based RF schemes. The BMMA differentiates positive feedbacks from negative ones based on local analysis, whereas the SemiBMMA can effectively integrate information of unlabeled samples by introducing a Laplacian regularizer to the BMMA. We formally formulate this problem into a general subspace learning task and then propose an automatic approach of determining the dimensionality of the embedded subspace for RF. Extensive experiments on a large real-world image database demonstrate that the proposed scheme combined with the SVM RF can significantly improve the performance of CBIR systems.

Content-Based Image Retrieval (CBIR) has attracted substantial attention during the past few years for its potential practical applications to image management. A variety of Relevance Feedback (RF) schemes have been designed to bridge the semantic gap between the low-level visual features and the high-level semantic concepts for an image retrieval task. Various Collaborative Image Retrieval (CIR) schemes aim to utilize the user historical feedback log data with similar and dissimilar pairwise constraints to improve the performance of a CBIR system. However, existing subspace learning approaches with explicit label information cannot be applied for a CIR task, although the subspace learning techniques play a key role in various computer vision tasks, e.g., face recognition and image classification. In this paper, we propose a novel subspace learning framework, i.e., Conjunctive Patches Subspace Learning (CPSL) with side information, for learning an effective semantic subspace by exploiting the user historical feedback log data for a CIR task. The CPSL can effectively integrate the discriminative information of labeled log images, the geometrical information of labeled log images and the weakly similar information of unlabeled images together to learn a reliable subspace. We formally formulate this problem into a constrained optimization problem and then present a new subspace learning technique to exploit the user historical feedback log data. Extensive experiments on both synthetic data sets and a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of a CBIR system by exploiting the user historical feedback log data.

Biased discriminant analysis (BDA) is one of the most promising relevance feedback (RF) approaches to deal with the feedback sample imbalance problem for content-based image retrieval (CBIR). However, the singular problem of the positive within-class scatter and the Gaussian distribution assumption for positive samples are two main obstacles impeding the performance of BDA RF for CBIR. To avoid both of these intrinsic problems in BDA, in this paper, we propose a novel algorithm called generalized BDA (GBDA) for CBIR. The GBDA algorithm avoids the singular problem by adopting the differential scatter discriminant criterion (DSDC) and handles the Gaussian distribution assumption by redesigning the between-class scatter with a nearest neighbor approach. To alleviate the overfitting problem, GBDA integrates the locality preserving principle; therefore, a smooth and locally consistent transform can also be learned. Extensive experiments show that GBDA can substantially outperform the original BDA, its variations, and related support-vector-machine-based RF algorithms.

Video enhancement has played very important roles in many applications. However, most existing enhancement methods only focus on the spatial quality within a frame while the temporal qualities of the enhanced video are often unguaranteed. In this paper, a new algorithm is proposed for video enhancement. The proposed algorithm introduces new temporal constraints and combines them with the spatial constraints such that both the spatial and temporal qualities of the video can be improved. Two strategies are proposed for including the temporal constraints. Experimental results demonstrate the effectiveness of the proposed algorithm.

Electro-magnetic Detection Satellite (EDS) is an important branch of Earth Observation Satellites (EOSs). It has been widely applied in industry and military areas. The detecting duration of EDS is different from imagery satellites, for it is an imprecise parameter because of the uncertain electromagnetic environment within space surrounding targets. This factor makes the task scheduling for EDS becoming a complex combinatorial optimization problem. With consideration of this special property, we used fuzzy set and possibility theory to model imprecise parameters, other physical constraints, like on-board energy and transition time between different working patterns and sensor's rebooting, were also taken into account in the model. We presented an improved genetic algorithm to solve this problem by introducing new method of elitist and parents selection. The model and the algorithm have been tested by five experiments derived from STK's satellite database.

In this paper, we introduce Lamarckian learning theory into the clonal selection algorithm and propose a sort of Lamarckian clonal selection algorithm, termed as LCSA. The major aim is to utilize effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compared LCSA with the clonal selection algorithm (CSA) in solving twenty benchmark problems to test the performance of LCSA. The results demonstrate that LCSA is effective and efficient in solving numerical optimization problems.

In this paper, based on Baldwin effect, an improved clonal selection algorithm, Baldwin clonal selection algorithm, termed as BCSA, is proposed to deal with complex multimodal optimization problems. BCSA evolves and improves antibody population by three operations: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. By introducing Baldwin effect, BCSA can make the most of experience of antibodies, accelerate the convergence, and obtain the global optimization quickly. In experiments, BCSA is tested on four types of functions and compared with the clonal selection algorithm and other optimization methods. Experimental results indicate that BCSA achieves a good performance, and is also an effective and robust technique for optimization.

Based on the theory of clonal selection in immunology, by introducing Baldwin effect, an improved clonal selection algorithm, termed as Baldwin clonal selection algorithm (BCSA), is proposed to solve the optimal approximation of linear systems. For engineering computing, the novel algorithm adopts three operations to evolve and improve the population: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new algorithm have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm, multi-agent genetic algorithm and artificial immune response algorithm.

In this study, we introduced a novel multiobjective optimization algorithm, Nondominated Neighbor Immune Algorithm (NNIA), to solve the multiobjective clustering problems. NNIA solves multiobjective optimization problems by using a nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators and elitism. The main novelty of NNIA is that the selection technique only selects minority isolated nondominated individuals in current population to clone proportionally to the crowding-distance values, recombine and mutate. As a result, NNIA pays more attention to the less-crowded regions in the current trade-off front. The experimental results on seven artificial data sets with different manifold structure and six real-world data sets show that the NNIA is an effective algorithm for solving multiobjective clustering problems, and the NNIA based multiobjective clustering technique is a cogent unsupervised learning method.